1. Field of the Invention
The present invention generally relates to supply management, and in particular to techniques for optimizing supply management for the assembly of expensive products.
2. Background Description
There are numerous prior art methods and systems that manage inventory. Since inventory problems can show substantial variations depending on the industry, process, supply network, and product characteristics, there is a proliferation of academic publications as well as patents covering inventory management.
U.S. Pat. No. 7,016,764 to Penkar et al., “Inventory management system for reducing overall warehouse and pipeline inventory” discloses an inventory management system for warehouse and pipeline inventory. The system is designed to provide regular shipments of parts to multiple manufacturing facilities from a hub via an expedited delivery service. The objective of the design is to reduce the amount of safety stock needed to support the operations of the manufacturing facilities.
U.S. Pat. No. 6,983,189 to Lu, “Systems and methods for manufacturing a product in a pull and push manufacturing system and associated methods and computer program products for modeling the same” has a system of manufacturing a product in a pull and push planning environment where products consist of multiple subassemblies which have multiple components. The system includes an order scheduler, a component scheduler, a make-to-order (MTO) portion and an assemble to order (ATO) portion. This is done for multi-plant systems.
U.S. Pat. No. 6,970,756 to Levionnois, “Computer-assisted pull flow production management method” discloses a method to manage component inventory using kanbans.
U.S. Pat. No. 6,594,535 to Costanza, “Material and inventory control system for a demand flow process” discloses a material flow design computer system that designs and monitors material to the production paths of a manufacturing line using a replenishment card pull sequence. Its objective is to monitor the product flow through replenishment pull sequence so as to insure that material arrives as needed at the point-of-usage on the production path and that the quantity of material stored at the manufacturing plant is minimized.
U.S. Pat. No. 6,516,301 to Aykin, “Order-based material management system” has an order based materials management method using forecasts of actual customer orders to determine component stocking levels. Orders are specified by bills of materials. Quantities of the components required in the bill of material have multivariate probability distributions and they can be correlated. The method calculates target number of replenishment orders for components using target order fill rate, replenishment lead times, demand forecasts, forecast error variances and their distributions. The method also calculates order-up-to periodic inventory policies (including safety stocks) for components and subassemblies.
U.S. Pat. No. 5,963,920 to Rose et al., “Inventory control system and method”, discloses a method and system for managing inventory of bulk commodities from a remote location. A rack storage unit at the point of use of the parts has multiple levels each having side by side rows for storing boxes of the parts. The rows are inclined downwardly from back to front to effect gravity feeding of the boxes toward the front. Each row has plural sites which may be occupied by a box, and a sensor senses whether a box is present at or absent from each site. Electrical signals from the sensors are processed and transmitted to the parts supplier at a remote location. The supplier receives a display containing information as to which sites are occupied and which are vacant. The supplier can respond by shipping parts that are indicated to be in short supply.
U.S. Pat. No. 5,946,662 to Ettl et al., “Method for providing inventory optimization”, discloses a method for providing inventory optimization for levels of products in a complex supply chain network for multiple internal suppliers or manufacturer locations and external distributors or retailer locations. The invention constructs a representative supply chain network model to indicate the flow of products between internal and external locations, it determines inventory levels and fill rates to meet the service level requirements, calculates a total inventory cost for all products in the network, and optimizes the fill rates.
U.S. Pat. No. 5,630,070 to Dietrich et al., “Optimization of manufacturing resource planning”, discloses a method for constrained material requirements planning, optimal resource allocation, and production planning provides for an optimization of a manufacturing process by designating the amounts of various manufactured products to be produced, which products include both end products as well as subassemblies to be employed in the manufacture of one or more of the end products. In order to accomplish the optimization, the method employs an objective function such as the maximization of income. In the method, the data describing elemental steps in the manufacturing process for the production of each end product, as well as the quantity or demand for each end product which is to be supplied, are presented as a set of linear mathematical relationships in matrix form to be inserted in a computer which determines the optimum number of each end product in accordance with an LP optimization algorithm. The matrix contains bill of material data, and various constraints.
U.S. Pat. No. 6,415,266 to Do, “Dynamic instruction system for input of parts in vehicle production line”, discloses a dynamic instruction system for the input of parts in a vehicle production line which, based on vehicle type information at each process as well as dynamic production plans, performs computations on a variety of data in order to provide parts input information to both equipment in the production line and to workers such that parts can be provided as they are needed. The system is claimed to prevent the presence of defective parts at processes in the production line and in a parts storage area, and to provide optimum stock levels such that capacity utilization and productivity are enhanced.
U.S. Pat. No. 5,819,232 to Shipman, “Method and apparatus for inventory control of a manufacturing or distribution process”, discloses a method and an apparatus, using a computer model, to control a manufacturing or distribution process, which determines a demand forecast by using an optimized historical weighting factor, determines an upper and a lower bound of a planned inventory by explicitly accounting for the customer order lead time, and computes a production schedule at predetermined intervals to maintain an actual inventory between the upper and lower bounds of the planned inventory.
U.S. Pat. No. 5,611,051 to Pirelli, “Point of supply use distribution process and apparatus”, discloses a method of using a central computer and work station for managing inventory. To dispense inventory to consumers, a system user is issued a card imprinted with a personal user bar code. This user bar code is read into the computer and subsequently verified through the entry of individual user access data to ensure the user is authorized. The issued-item data is communicated to a central computer, where the issued item is decremented from inventory data. The central computer determines when and in what quantity to replenish each item from the inventory data. A replenishment order is processed by the central computer, and the replenishment order is transmitted to a vendor.
U.S. Pat. No. 5,287,267 to Jayaraman et al., “Methods for parts procurement quantity determination where demand is uncertain for the product in which the parts are used”, discloses a method for predicting parts procurement requirements for products over multiple time periods, with parts common to multiple products. The actual demand for the products is uncertain, but the method assures that a specified service level is met for all products and minimizes expected excess part inventories. The method is provided with inputs, which, among others, includes lists of parts for each product, prices for the parts, and demand forecasts for each product in each time period, each forecast in the form of a mean and standard deviation. The description of the problem includes an objective function of minimizing expected excess inventory while satisfying the constraint that a specified service level be achieved. Solution is provided using Lagrange multiplier technique. Additional methods are described for improving the procurement decisions to more closely meet the service requirement.
In all of the above patents (with the exception of U.S. Pat. No. 6,983,189, which is discussed in the next paragraph) the methods disclosed are for managing inventory volumes to support some manufacturing or distribution activity. Managing inventory volumes means using policies such as base-stock levels, lot size or order-up-to levels. Such methods are quantity driven. In other words, they trigger replenishment orders when inventory levels drop to a threshold quantity. They are appropriate methods when usages of components, products or parts are at high volumes. However, quantity-based inventory policies do not perform well for scheduling manufacturing assembly of complex products, simply because they do not allow triggering replenishment orders at any point in time; they trigger orders only if inventory levels drop to a threshold level. For instance, if a component is needed once every 2 months, quantity-based policies will wait an average of 2 months to trigger an order while it might be better to time the order more accurately sometime within the 2 month period. Furthermore, when there are thousands of components that have to come together to perform an assembly, the quantity-based safety stocks can be prohibitively high. For instance, consider a complex product that needs a thousand different types of components for its assembly. If the assembly has to start on time with 99% probability and supply lead times for components are uncertain, then each component has to have a safety factor of at least 4.25. This requires extremely high levels of component inventories and therefore may be too costly. In such cases, there is a need for a fundamentally different method to managing inventory.
U.S. Pat. No. 6,983,189 is an exception to the others mentioned above. It generates order schedules. Generating some kind of supply order schedule is not new. There are procurement systems that suggest order times. For instance, standard MRP also schedules component orders but ignores the impact of uncertainty in lead times. Furthermore, MRP is quantity based (i.e. schedules orders of lots), and it does not focus on the best time to trigger an order for an individual part when managing a low volume manufacturing environment.
What is needed is a method for effectively managing supply for products that are expensive to assemble such as aircraft, heavy equipment and machinery. Supply inventory management of such products needs a different approach than those described in the prior art. Known techniques that are implemented in commercially available software are not adequate. Known techniques are designed for high volume products that tend to be less expensive and typically sell in hundreds or thousands in weekly volume.
However, for highly expensive items such as aircraft or heavy machinery, weekly volumes for each model or part number tend to be much smaller. Even the largest manufacturers make just a few items per week. In addition, due to product complexity, there are tens of thousands of components that have to be supplied to do the manufacturing. Furthermore, replenishment lead times for these components are uncertain. Therefore, management of component supply needs a different approach for such products. The challenge is twofold: 1) how to make sure that random variation in supply replenishment lead times does not disrupt the manufacturing activity due to supply shortages, and 2) how to minimize supply safety stock so that supply inventory turns are maximized.
In general, these two objectives are in conflict for any inventory management problem. For expensive items, the problem is particularly challenging. On the one hand, because expensive items tend to have an expensive manufacturing operation, maximizing manufacturing line utilization is an important goal. Therefore, supply must be available at very high levels of confidence. On the other hand, because tens of thousands of components must to be available simultaneously, each component must have extremely high levels of availability to support smooth operations of the manufacturing line. When random variation of supply replenishment lead times is added to this statement of the problem, one is required under conventional safety stock management methods to hold very high levels of component supply. But this is prohibitively expensive for a product which must be assembled from a very large number of component parts. An efficient process needs to be put in place in order to manage supply inventories at reasonable levels and still achieve smooth manufacturing operations for such products.